Volume 3, Issue 6, December 2014, Page: 80-88
Stochastic Simulation of Shallow Aquifer Heterogeneity and It’s Using in Contaminant Transport Modeling in Tianjin Plains
Lingling Liu, College of Environmental Science and Engineering, Nankai University, Tianjin, China
Lixin Yi, College of Environmental Science and Engineering, Nankai University, Tianjin, China
Xiaoqing Cheng, College of Environmental Science and Engineering, Nankai University, Tianjin, China
Received: Nov. 26, 2014;       Accepted: Dec. 23, 2014;       Published: Dec. 29, 2014
DOI: 10.11648/j.wros.20140306.13      View  3167      Downloads  171
Shallow aquifers of Tianjin Plain formed by alluvium, marine and lacustrine sedimentary sequences, and resulting complex structure impose challenges to modeling groundwater flow and contaminant transport in it. To solve the problem and prove its feasibility, this study utilizes TProGS (Transition Probability Geostatistical Software) to describe hydrogeological structure of engineering sites, and then simulates contaminant transport by integrated using MT3D (Modular Three-Dimensional Transport Model) with traditional layered assignment approach and transition probability geostatistical approach respectively. The results show that aquifer structure on local scale is effectively described by TProGS and there is a smaller plume distribution in modeling with transition geostatistical approach than that with traditional layered assignment approach, it’s also more in line with the groundwater flow direction. It illustrates the advantages of stochastic simulation in detailed conceptualization of hydrogeological structure. Furthermore, it demonstrates that integrated utilizing stochastic simulations and MT3D is more practicable than traditional approach in engineering practice for both probabilistic estimation of hydraulic conductivities and probabilistic assessment of contaminant plume capture at a heterogeneous field site.
Stochastic Simulation, Heterogeneity, TProGS, MT3D, Tianjin Plains
To cite this article
Lingling Liu, Lixin Yi, Xiaoqing Cheng, Stochastic Simulation of Shallow Aquifer Heterogeneity and It’s Using in Contaminant Transport Modeling in Tianjin Plains, Journal of Water Resources and Ocean Science. Vol. 3, No. 6, 2014, pp. 80-88. doi: 10.11648/j.wros.20140306.13
Aarts E, Korst J (1989) Simulated annealing and Boltzmann machines: A stochastic approach to combinatorial optimization and neural computing. John Wiley & Sons, New York
Anderman ER, Hill MC (2000) The U.S. geological survey modular groundwater model-Documentation of the hydrogeological unit flow (HUF) package. U.S. Geological Survey Open-File Report 00–342
Arciprete D, Bersezio R, Felletti F, Giudici M, Comunian A, Renard P (2012) Comparison of three geostatistical methods for hydrofacies simulation: a test on alluvial sediments.Hydro 20:299–311
Carle SF (1996) A transition probability-based approach to geostatistical characterization of hydrostratigraphic architecture. Dissertation, University of California
Carle SF (1997) Implementation schemes for avoiding artifact discontinuities in simulated annealing. Math Geol29:231–244
Carle SF (1999) T-PROGS: Transition probability geostatistical software. Univ of Calif, Davis
Carle SF, Fogg GE (1996) Transition probability-based indicator geostatistics. Math Geol28:53–476
Carle SF, Weissmann GS, Fogg GE (1998) Conditional simulation of hydrofacies architecture:a transition probability approach/Markov approach. In: Hydrogeologic models of sedimentary aquifers. SEPM Spec Publ.pp147-170
Clement TP (1997) RT3D:A modular computer code for simulating reactive multi-species transport in 3-dimensional groundwater aquifers. Pacific Northwest National Laboratory, Washington
Deutsch CV, Cockerham PW (1994) Practical considerations in the application of simulated annealing to stochastic simulation. Math Geol 26:67–82
Deutsch CV, Journel A (1992) GSLIB: Geostatistical Software Libary and Users Guide. Oxford University Press, New York
Engdahl NB, Vogler ET, Weissmann GS (2010) Evaluation of aquifer heterogeneity effects on river flow loss using a transition probability framework. Water Resour Res. doi:10.1029/2009WR007903.
Koch J, He X, Jensen KH, Refsgaard JC (2013) Challenges in conditioning a stochastic geological model of a heterogeneous glacial aquifer to a comprehensive soft dataset. Hydrol Earth Syst Sci Discuss 10:15219-15262
Ritzi RW (2000) Behavior of indicator variograms and transition probabilities in relation to the variance in lengths of hydrofacies. Water Resour Res. 36:3375–3381
Seifert D, Jensen, JL (1999) Using sequential indicator simulation as a tool in reservoir description:issues and uncertainties. Math Geol 31:527–550
Weissmann GS,Fogg GE (1999) Multi-scale alluvial fan heterogeneity modeled with transition probability geostatistics in a sequence stratigraphic framework. Hydrol 226:48–65.
Walker JR (2002) Application of transition probability geostatistics for indicator simulations involving the MODFLOW model. Dissertation, Brigham Young University
Zheng CM (1990) MT3D:A modular three-dimensional transport model for simulation of advection, dispersion and chemical reactions of contaminants in groundwater systems. U.S.Environmental Protection Agency
Zheng CM, Wang PP (1999) MT3DMS:A modular three-dimensional multi-species transport model for simulation of advection, dispersion, and chemical reactions of contaminants in groundwater systems: documentation and user’s guide. U.S. Army Engineer Research and Development Center
Browse journals by subject